Evaluating meteorological comparability in air quality studies: Classification and regression trees for primary pollutants in California’s South Coast Air Basin W. Choi a , S.E. Paulson a , J. Casmassi b , A.M. Winer c, * a Department of Atmospheric and Oceanic Sciences, 405 Hilgard Ave., University of California, Los Angeles, CA 90095-1565, USA b Planning, Rule Development and Area Sources, California South Coast Air Quality Management District, 21865 Copley Drive, Diamond Bar, CA 91765-4178, USA c Environmental Health Sciences Department, School of Public Health, 650 Charles E. Young Drive South, University of California, Los Angeles, CA 90095-1772, USA highlights < Regression trees for primary pollutants were created using a CART model. < Primary pollutant levels are largely under control of meteorology in the SoCAB. < The most important meteorological variables are wind speed and geopotential height. < Spatial variances in pollutants are well correlated with meteorological conditions. < CART analysis provides an effective tool for meteorological comparability. article info Article history: Received 4 April 2012 Received in revised form 1 September 2012 Accepted 19 September 2012 Keywords: Primary pollutants Meteorological adjustment Traffic emissions Meteorological comparisons abstract Meteorology confounds the comparison of air quality data across time and space. This presents chal- lenges, for example, to comparisons of pollutant concentration data obtained with mobile monitoring platforms on different days and/or locations within the same airshed. In part to address this challenge, we employed a classification and regression tree (CART) modeling approach that can serve as a useful and straightforward tool in such air quality studies, to determine the comparability of meteorological conditions between measurement days and locations as well as to compare primary pollutant concen- trations corrected by meteorological conditions. Specifically, regression trees were developed to obtain representative concentrations of traffic-related primary air pollutants such as NO x and CO, based on meteorological conditions for 2007e2009 in the California South Coast Air Basin (SoCAB). The resulting regression trees showed strong correlations between the regression classifications developed for different pollutant metrics, such as daily CO and NO x maxima, as well as between monitoring sites. For the SoCAB, the most important meteorological parameters controlling primary pollutant concentrations were the mean surface wind speed, geopotential heights at 925 mbar, the upper air northesouth pressure gradient, the daily minimum temperature, relative humidity at 1000 mbar, and vertical stability, in approximate order of importance. The value of developing a regression tree for a single season was also explored by performing CART analysis separately on summer data. Although seasonal classifications were similar to those developed from annual data, the standard deviations of the classi- fication groups were somewhat reduced. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction A growing number of epidemiological studies have shown that exposure to fresh vehicular emissions causes adverse human health effects, including asthma, cardiovascular disease, and adverse birth outcomes (Hoek et al., 2010; Penttinen et al., 2001; Pope et al., 2002). Ultrafine particles (UFP), along with other traffic-related pollutants including nitrogen oxides (NO x ), carbon monoxide (CO), and various organic gases emitted near major roads, are of particular interest in metropolitan areas, including the California South Coast Air Basin (SoCAB). Despite enormous progress in reducing air pollution over the past four decades, the SoCAB remains one of the most polluted regions in the U.S., with mobile sources accounting for 93% and 89% of the total annual emissions of CO and NO x , respectively, as of 2008 (CARB, 2009). * Corresponding author. Tel.: þ1 310 206 4442; fax: þ1 310 206 3358. E-mail addresses: wschoi@atmos.ucla.edu (W. Choi), paulson@atmos.ucla.edu (S.E. Paulson), jcassmassi@aqmd.gov (J. Casmassi), amwiner@ucla.edu (A.M. Winer). Contents lists available at SciVerse ScienceDirect Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv 1352-2310/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2012.09.049 Atmospheric Environment 64 (2013) 150e159